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Gonçalves, P., F. Grilo, R. C. Mendes, K. Vierikko, B. Elands, T. A. Marques, and M. Santos-Reis. 2021. What’s biodiversity got to do with it? Perceptions of biodiversity and restorativeness in urban parks. Ecology and Society 26(3):25.

What’s biodiversity got to do with it? Perceptions of biodiversity and restorativeness in urban parks

1Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências (cE3c, FCUL), Universidade de Lisboa, Lisbon, Portugal, 2Environmental Policy Centre, Finnish Environment Institute (SYKE), 3Forest and Nature Conservation Policy Group, Wageningen University & Research, 4Centre for Research into Ecological and Environmental Modelling, The Observatory, University of St Andrews, 5Centro de Estatística e Aplicações, Faculdade de Ciências (CEAUL, FCUL), Universidade de Lisboa, Lisbon, Portugal


Understanding the mechanisms underlying the positive influence of urban biodiversity on dwellers’ well-being is critical to inform sustainable management of urban greenspaces, but studies published to date on the role of biodiversity in mental restoration have provided contradictory results. Both urban greenspace biophysical characteristics and the emotional connection with the place may play a role in mental restoration. Using urban greenspace landscape and biodiversity metrics, and on-site observations and questionnaires to assess site quality and visitors’ perceptions, we explore the influence of biodiversity (perceived and measured at different levels), urban greenspace characteristics, and visitors profiles in mental restoration. Our statistical analysis demonstrates that perception of biodiversity, along with satisfaction and connection to place, were the most important predictors of mental restoration. The proportion of broadleaf and evergreen tree species also had a positive influence on biodiversity perception and mental restoration. People perceive existing biodiversity through visual cues as the diversity of leaf forms in broadleaf species, with this process being almost completely mediated by the perception of tree diversity. These findings have direct translation into planning and management practices by acknowledging the importance of biodiversity and, above all, specific traits, namely of evergreen broadleaf species, in promoting restorativeness of urban parks for users. Our results highlight the importance of using standard ecological methods when assessing biodiversity in urban greenspaces and their influence on human well-being.
Key words: biodiversity perception; biological diversity; functional diversity; mediation; mental well-being; perceived restorativeness


Living in cities can be very stressful. Crowding, high levels of noise, strong artificial lights and odors, pollution, and many other factors contribute to people becoming overloaded with undesired stimuli, which can have consequences for mental health and well-being (Galea et al. 2005). Interacting with nature may help to overcome these effects through a wide range of benefits. Reduced anxiety and stress, and improved cognitive abilities, self-esteem, happiness, and life satisfaction are just some of the benefits of interacting with nature (Frumkin et al. 2017, Hall and Knuth 2019a,b). According to previous research, mental restoration has been consistently associated with positive aspects of well-being (Marselle et al. 2019). This process is known as restorativeness, which is defined as the potential of the environment to re-establish certain cognitive capacities related to human information processing (Hartig 2004).

Natural environments in cities are representative of human intervention in the landscape over time. They can include remnant areas of natural habitats that may be more or less free of human intervention, as well as man-made or transformed habitats, with a multitude of designs, architectural elements, and vegetation structures. Natural environments are generally more restorative than urban ones, but built elements in urban greenspaces may also be a source of psychological restoration and well-being (Lindal and Hartig 2013, Hajrasoulih et al. 2018, Weber and Trojan 2018).

Urban greenspaces, specifically urban parks, are complex social-ecological systems that serve both as biodiversity repositories that perform ecological functions and as spaces for mingling that fulfill important social functions (Cattell et al. 2008, Elmqvist et al. 2013). Each urban greenspace has its own biological and cultural constellations that offer meaning and identity to the space (Andersson and Barthel 2016). People perceive, experience, and value a space according to their perceptions and attached emotions. Perceptions are crucial for interpreting and assigning meaning to the environment (Qiong 2017). A space recognized as meaningful and with which people can identify induces intangible, emotional, and cognitive bonds (Tuan 1975) that positively influence mental restoration and well-being (Ratcliffe and Korpela 2016, Knez et al. 2018). In addition, according to constructivist approaches, people construct objects of perception and thought that contribute to the cognitive process, and reality is an interpretation of the environment within this perspective (Wapner and Demick 2002). This assumption implies a distinction between the experienced and physical environment, and it highlights the importance of assessing people’s perceptions when dealing with mental well-being. The effects of perceptions of naturalness, greenness, and aesthetic attractiveness on mental well-being have been investigated previously (e.g., Hidalgo et al. 2006, Marselle et al. 2015, Hipp et al. 2016), but to the best of our knowledge, no study has jointly considered the roles of biodiversity (which we define as the richness of species and variability of species traits), both objective and perceived, and the physical characteristics of parks in well-being.

Evidence supports the positive effect of experiencing nature on mental well-being (Houlden et al. 2018, Menardo et al. 2021), but little is known about the specific roles of different attributes of nature, i.e., biodiversity, or the mechanisms that might allow them to exert their influence (Botzat et al. 2016, Marselle et al. 2019). Few studies have investigated the relationship between biodiversity and mental well-being, and those that have often simply compared non-urban natural environments with urbanized environments or assessed nature in terms of naturalness, greenness, and other proxies. Only a minority of these studies assessed biodiversity using standard ecological methods and metrics (Botzat et al. 2016, Marselle et al. 2019), and the results were both mixed and varied across scales ranging from species to ecosystems. For example, some studies of habitat diversity and bird species richness found positive associations with mental well-being (Fuller et al. 2007, Luck et al. 2011, Johansson et al. 2014, Rantakokko et al. 2018), whereas the associations were not significant or inconclusive in other studies (Dallimer et al. 2012, Wheeler et al. 2015, Wolf et al. 2017). These contradictory results have led to some authors arguing that perceived biodiversity, rather than actual biodiversity, is responsible for mental well-being (Dallimer et al. 2012), and they have questioned the ability of lay-people to perceive variations in biodiversity accurately. Associations between perceived biodiversity and mental well-being were described as both positive (Dallimer et al. 2012, Schebella et al. 2019) and non-significant (Marselle et al. 2015, 2016), and the same contradiction was found when testing the ability of lay-people to assess existing biodiversity accurately (Fuller et al. 2007, Lindemann-Mathies and Bose 2008, Dallimer et al. 2012). Some authors have argued that large variations at the ecosystem level, such as habitat heterogeneity or vegetation cover, are more likely to be recognized than subtle variations in species diversity (Dallimer et al. 2012, Schebella et al. 2019). Many studies of the perception of biodiversity were also based on coarse definitions of biodiversity itself (e.g., greenness or naturalness) rather than species diversity. Perceived biodiversity is likely to depend on the visible traits of species that allow the discrimination of differences (de Vries and Snep 2019), but no previous study has explored the influence of visible functional traits on the perception of biodiversity or mental well-being. Thus, it is necessary to clarify the biodiversity elements that are responsible for mental well-being and the mechanisms that might allow these elements to contribute to well-being using standard metrics of landscape structure and biological and functional diversity, which are the most effective and accurate methods for achieving these aims.

In addition to natural elements, the facilities and amenities found in urban greenspaces contribute to the association between visiting greenspaces and mental restoration (Nordh and Østby 2013). Cultural and art elements with high aesthetic value also have important restorative attributes (Deng et al. 2020). However, few integrated studies have considered the effect of both biodiversity and park characteristics on well-being (Wood et al. 2018).

Consequently, we aimed to understand two facets of urban greenspaces, specifically focusing on urban parks (including the qualities of both parks and biodiversity). First, how do the characteristics of urban greenspaces, the perceptions of these characteristics by users, visitation patterns, and connection to place contribute to mental well-being? Second, how is the perception of existing biodiversity related to objectively quantified biological and functional diversity?


Twelve urban parks with public access in Lisbon, Portugal were studied as part of the EU FP7 GREENSURGE research project ( The parameters used to select the study parks included their location in the city, a diversity of designs and facilities, area ≥ 4 ha, and age > 40 yr to ensure a completely developed vegetation structure (Table 1). A map showing the locations and pictures of each park may be accessed at More detailed information about the characteristics of the parks, specifically the number of biotopes, vegetation structure, and presence of facilities and cultural artifacts, is provided by Vierikko et al. (2020).

Data collection

Fifty face-to-face questionnaires were completed in each park to assess the users’ perceptions of the parks’ meaningfulness, attribute values, and biodiversity. The questionnaires were completed in the early morning or late afternoon during weekdays, and during any daytime hours on the weekends, while avoiding extreme weather conditions (> 30°C) and public events in the parks. Interviewers searched for respondents throughout the entire park to include people engaged in all possible activities and visitors with different stay lengths. In parks with low visitation rates, every visitor who entered the park was interviewed, whereas every fifth visitor was interviewed in other parks. We briefly explained the study’s aim to each visitor we approached and asked them to participate. In total, 610 visitors (44.1% female and 55.9% male) > 18 years old and currently living in the city responded to the questionnaire (each lasted at least 10 min), and 80 refused to participate or did not complete it. The questionnaire covered several topics related to both visits and visitors, and further details are available in the study by Vierikko et al. (2020). The perceived restorativeness and biodiversity perception were evaluated using variables related to both the uses and the site as follows (Fig. 1).

Perceived restorativeness

We assessed mental restoration using a reduced version of the Perceived Restorativeness Scale, which is a tool developed by Hartig et al. (1997) for assessing the restorative qualities of environments based on attention restoration theory (Kaplan and Kaplan 1989). This theory posits that nature offers a bottom-up stimulus that allows fatigued directed attention to rest and restore by capturing one’s involuntary attention. To achieve this effect, the natural environment must provide the feeling of being away and distant from daily routines, provide sufficient area to explore and experience a visually organized and harmonious environment, be compatible with one’s needs and desires by offering the opportunity to engage in enjoyable and meaningful activities, and be fascinating and attract direct attention in an effortless manner. We used eight items (Table 2) from the previously tested Perceived Restorativeness Scale survey instrument (Pasini et al. 2009), which were shown to be valid in previous studies (e.g., Scopelliti et al. 2012, Carrus et al. 2013). Two items from each of the Perceived Restorativeness Scale dimensions were used, and visitors were asked to score their opinion on a Likert scale ranging from 1 (do not agree) to 5 (completely agree). The individual scores were summed and averaged (final aggregated score) and then treated as a continuous variable in subsequent analyses (Norman 2010). Perceived restorativeness (PR) was used as a composite measure to assess the four experiential restorative qualities. The internal consistency of the measure was confirmed by a Cronbach’s alpha score of 0.73.

Biodiversity perception

To assess overall biodiversity perception (BDP), visitors were asked whether they perceived high levels of biodiversity (yes or no). Most visitors answered “yes” or “no,” but some provided intermediate answers such as “so-so,” “not so much,” or “only plants, not animals.” These responses were pooled together with the negative answers, and BDP was analyzed as a binary variable. To understand whether biodiversity, either real or perceived, was an important attribute of PR, the same taxa were used for comparison in both assessments. Vascular plants, butterflies, and birds were selected because they are the most conspicuous taxa in parks and are readily recognized by lay-people, as demonstrated by citizen science databases (e.g., iNaturalist, and the interviewees were also asked to score the perceived diversity of each taxon separately on a scale from 1 (very low) to 5 (very high).

User variables

Patterns of visitation were determined by asking how often respondents visited a space and how long they stayed. Frequency of visitation (“frequency”) was classified according to two categories comprising occasional (less than once a week) or regular visitors (at least once a week). Duration of visit (“duration”) was classified according to five groups: passing by, < 30 min, 30 to 90 min, 90 to 180 min, and > 180 min. The “age” and “education” of users were requested to determine basic sociodemographic strata. The perception of satisfaction with a space was assessed based on the number of “complaints” reported about park maintenance and state of preservation. The meaningfulness attributed to a place, which was determined as the existence of an emotional connection, was assessed by asking visitors whether the park was special for them (“yes” or “no”).

Site variables

We evaluated each park’s overall quality by assessing a set of on-site conditions, including the number and variety of amenities and facilities, their state of preservation, overall cleanliness, and absence of vandalism. Each item was scored from 1 (low variety/condition of amenities/facilities and visible signs of litter/incivilities) to 5 (high number/condition of amenities/facilities and no visible signs of litter/incivilities) and summed to obtain a final space quality score (“park quality”).

The biological characteristics of the parks included land cover and structure mapping, biodiversity assessments, and the visible functional traits of tree species. The land cover in parks was mapped on-site with MapItGIS for Android (MapitGIS Walk-Through Patches were delimited and classified according to cover type: trees with or without shrubby understory in various densities, grass or lawn, social areas (e.g., playgrounds, cafeterias, sport courts), water bodies, and paths. Patches with tree cover were also classified according to the dominant tree species. A geographic information system project was built in QGIS 2.18.18 (QGIS Development Team 2019), and land-cover metrics were calculated for parks with FRAGSTATS software (McGarigal et al. 2012) using two sets of classifications based on the cover type and dominant tree species. Four metrics for measuring landscape fragmentation, complexity, diversity, and evenness were computed for both classifications. The normalized difference vegetation index (“NDVI”) was computed based on Sentinel2A images with a spatial resolution of 10 m for the summer months.

Vegetation assessments were conducted according to a stratified sampling scheme based on the cover types and their area of occupancy in which transects were drawn in the patch centroid along the longest length. The area sampled for each cover type was at least 5% of its occupancy area. Trees were sampled along the transect using the point-centered quadrat method (Elzinga et al. 2001). Butterflies were sampled along fixed transects that covered all different cover types according to the Manual of Butterfly Monitoring issued by Butterfly Conservation Europe (Van Swaay et al. 2012). Parks were visited once each month from June to August, and all butterflies detected within 2.5 m on either side of a transect and 5 m in front of the observer were identified and recorded. Bird sampling was conducted during the breeding season (April–June) in the first hours after sunrise based on 10 min point counts in the park centroid and several extra points in proportion to the park’s size (Sutherland 2006) up to a maximum of three extra points. The species richness, Shannon diversity, and evenness indexes were calculated for each taxon.

Visible traits of trees were compiled from various databases such as the TRY Plant Trait database (; Kattge et al. 2011) and the Botanical Information and Ecology Network (; Enquist et al. 2016). These traits were used to calculate the functional structure and diversity of trees based on the community weighted mean (CWM) and functional dispersion (Laliberté and Legendre 2010) using the R package “FD” (Laliberté et al. 2014). The CWM provides information about the dominant traits in the community by calculating trait values weighted by species abundances; it represents the percentage of each trait in the community for categorical traits. Functional dispersion reflects the degree of dissimilarity within the community, whereby a higher value denotes a greater diversity of traits in the community. Functional diversity indexes were calculated for leaf type and phenology, and flower and fruit showiness and phenology.

All of the variables collected, as well as their descriptions, units, and P-values for univariate analysis, are provided in Appendix 1.

Data analysis

Because of the hierarchical structure of the data collected (parks and surveys within parks) and specific characteristics of each park, we constructed an initial generalized linear mixed model without predictors for both PR and BDP with “park” as a random effect to account for the variability associated with parks. No variance in PR was associated with park, so the variance associated with park was not significant when variables were added to the BDP model. Thus, we employed generalized linear models in subsequent analyses to test the effects of the biophysical features of parks on PR and BDP. PR was modeled as a gamma distribution with an inverse link, and BDP was modeled as a binomial distribution with a logit link. To build the generalized linear models, the significance of each variable was first assessed by univariate analysis, in which variables with P < 0.25 were selected for multivariate analysis (Hosmer and Lemeshow 2000). A Pearson’s correlation matrix was generated, and the correlated nature of our data set demanded a highly conservative approach to assure the non-collinearity of the predictors. If a correlation was significant (r > 0.6), we selected the variable that was least correlated with the remaining variables, with higher biological or social significance, with lower residual deviance according to univariate analysis, or with a variance inflation factor < 10 (Tabachnick and Fidell 1996).

The final set of variables used to build the models for PR and BDP was selected after all of the previously defined assumptions were satisfied and preliminary univariate analysis was performed (Table 3). We used the same set of variables for both models, except those related to park quality and patterns of visitation were not included in the BDP model, which was focused on biodiversity. For each model, all significant, non-collinear, and suitable variables were included in the initial model, and a backward stepwise procedure was employed to select the best model based on the lowest Akaike information criterion. Scatter plots were checked to confirm the adequacy of residuals, homogeneity of variance, linearity of predictors, and goodness-of-fit of model analysis (Hosmer and Lemeshow 2000). Partial regression and partial correlation coefficients were also calculated to measure the effect of each individual predictor in the PR model and the strength of the relationship, respectively, while keeping the other variables constant (Freund et al. 2010).

The absence of significant biodiversity-related variables in the final models for both PR and BDP led us to investigate whether any of the biodiversity metrics influenced PR or BDP through indirect pathways mediated by the perceptions of users. Mediation is a cause-effect model that aims to explain the intermediary process that allows an external event (independent variable) to influence the internal psychological significance (dependent variable) through a mediator (Baron and Kenny 1986). The effect of the independent variable on the dependent variable must be reduced or eliminated (partial or complete mediation) when the mediator is kept constant to demonstrate mediation (Hayes and Rockwood 2017). To qualify for mediation, in addition to theoretical support for the proposed mechanism, there must be significant relationships between the independent variable and mediator, and between the mediator and the dependent variable. Based on the constructivist approach for establishing a distinction between the measured and perceived environment, we used variables comprising the species richness; the richness, diversity, and evenness of tree species; CWM_broadleaf, CWM_evergreen, PRDsp (patch richness density of dominant tree species), and NDVI; as well as the mediators BDP, PR, and tree diversity perception to test the effects of perceptions in mediating the path between measured and perceived biodiversity (BDP), and between perceptions and PR. The analysis was performed using the R package “Psych” (Revelle 2018), which calculates the partial regressions and tests for the existence of mediation by using bootstrap methods to compute confidence intervals.


Significant positive correlations were found between PR and some of the biological diversity indexes such as total species richness (r = 0.129), butterfly species richness (r = 0.171), and diversity of birds (r = 0.087), whereas a higher evenness of tree species was negatively correlated with PR (r = −0.188; Table 3). Except for the diversity of tree patches (SHDIsp), which was positively correlated with PR (r = 0.137), the remaining land-cover metrics (PRDcov and PRDsp) had negative correlations with PR (r = −0.099 and r = −0.106, respectively). The visible functional traits related to leaf shape (CWM_broadleaf) and phenology (CWM_evergreen) were also significantly correlated with PR (r = 0.136 and r = 0.214, respectively), and NDVI was positively correlated with PR (r = 0.093). Users’ perceptions of existing biodiversity were all positively correlated with PR (BDP, r = 0.286; tree diversity perception, r = 0.161; bird diversity perception, r = 0.137) as well as the special connection with the place (special, r = 0.177). By contrast, when the number of complaints regarding the space was higher, the value of PR was lower (r = −0.177). According to the visiting profile, only the frequency of visits had a significant positive correlation with PR (r = 0.092). Correlation analysis for BDP also found some positive associations with diversity indexes such as total species richness (r = 0.147), butterfly species richness (r = 0.121), butterfly evenness (r = 0.148), and diversity of dominant tree species (SHDIsp, r = 0.142). Evenness of tree species (MSIEIsp) was negatively correlated with BDP (r = −0.087). CWM_broadleaf and CWM_evergreen were also positively correlated with BDP (r = 0.104 and r = 0.191, respectively), whereas fruit showiness was negatively correlated (r = −0.083). Among the perceptions of users, only the perceived tree and bird diversities were positively correlated with BDP, and both variables had large correlation coefficients (r = 0.410 and r = 0.310, respectively).

Among the biodiversity variables, only the patch richness density of cover types (PRDcov) and CWM_evergreen remained significant in the final model for PR (Tables 3 and 4). PRDcov is a measure of patch richness and also landscape fragmentation, whereby a higher value denotes a higher number of smaller patches. CWM_evergreen represents the proportion of evergreen tree species in a park. Thus, PR was negatively influenced by a fragmented landscape with a higher number of small patches and positively influenced by a higher proportion of evergreen species. The remaining significant predictors were related to pattern of visitation by users (frequency and duration of visit) and perceptions, whereby higher frequency and duration of visits both had positive effects on PR. Perceiving high levels of biodiversity (BDP) and having an emotional connection with the place (special) also had positive effects on PR, whereas a higher number of complaints about the space was associated with lower PR. Park quality, measured based on cleanliness and the variety and state of preservation of facilities and amenities, also had a positive effect on PR. The variables related to user perceptions (special, complaints, and BDP) had the greatest effect, and they had stronger relationships with PR according to the higher partial regression and correlation values. The measures of park quality and biodiversity (PRDcov and CWM_evergreen) had weaker effects and correlations with the variables related to visitation patterns, and they were also less influential predictors with weaker correlations (Table 4).

The perception of high levels of biodiversity had an important effect on PR, even when combined with other predictors, so we built a model for BDP based on the same set of biodiversity-related variables but excluding those related to park quality and visitation patterns. Many diversity-related variables had significant correlations according to univariate analyses, except for the proportion of evergreen species, but none of the other taxonomical, functional, or landscape diversity variables were retained in the final model. BDP was primarily determined by people’s perceptions of tree and bird diversity (Table 5), whereby tree diversity was the most significant predictor with the greatest influence and strongest correlation according to partial regression and the correlation coefficients.

We then tested whether any of the biodiversity metrics had an indirect effect on restorativeness through the perception of biodiversity, and whether this effect was mediated by the perception of tree species diversity. Because of the correlated nature of BDP and PR, we also tested the mediating role of PR in BDP (Table 6 and Fig. 2). For the indirect effect of biodiversity on restorativeness through BDP, we only found partial mediations ranging between 18% and 32% for species richness, tree evenness, and percentage of evergreen and broadleaf species, and NDVI had the highest mediation proportion (53%). The inverse pathway was also tested, and partial mediations (15–42%) were found for tree species evenness, percentage of broadleaf and evergreen species, and NDVI. However, for BDP, full mediation through tree perception was found only for CWM_broadleaf. All of the other tested mediations were not significant, according to the assumptions of the analysis (Hayes and Rockwood 2017).

Our results showed that mental restoration was primarily determined by the user perceptions, meaning, and value associated with parks, rather than by the biophysical attributes of the parks themselves, including their biodiversity. The main determinant for mental restoration was the perception of biodiversity, which was not directly influenced by any of the quantified metrics. BDP was indirectly determined by the amount of broadleaf species through the mediation of perceived tree species diversity.


This is the first study to integrate actual park attributes (biodiversity and park facilities) with user perceptions of these attributes and user visitation profiles. Our results showed that individual perceptions, or lived experiences, had more important effects on restorativeness than actual attributes. Perception-related variables, including BDP, emotional connection with the space, and complaints were most significant in the PR model. Moreover, the amount of variation explained by these variables was more than double that explained by the remaining set of variables related to biophysical measurements and visitation patterns according to the partial regressions.

Park quality, assessed based on amenity and facilities numbers and state of preservation as well as the level of park cleanliness, also contributed positively to PR, but to a much lesser extent. The presence of amenities and facilities that meet the needs and desires of visitors may contribute to a better appreciation of parks, and thus, to restorativeness (Nordh and Østby 2013, Wilkie and Stavidrou 2013). By contrast, evidence of litter or degraded furniture elicits undesired stimuli that attract attention, thereby impeding the feeling of being away, which is important for mental restoration according to attention restoration theory (Kaplan and Kaplan 1989). Cleanliness is one of the most desirable attributes in urban parks (Nordh and Østby 2013, Madureira et al. 2018), and the presence of litter is a major cause of the disturbances reported by users, as well as the lack of adequate management and complaints about the misbehavior of other users, including littering (Vierikko et al. 2020).

People seek urban greenspaces for many different reasons, and their needs and preferences for specific park features vary accordingly (Peschardt et al. 2016). The multiple uses, meanings, and needs regarding a single space will clearly lead to conflicts (Dinnie et al. 2013), especially if the space area and design do not adequately separate these multiple interests. Larger parks or those with a design that leads to a perception of large dimensions are related to a higher capacity for restoration (Nordh et al. 2009). A highly fragmented pattern with diverse land-cover types represented by high patch richness density may favor close contact with less desired environments and impair the ability to feel immersed (extent) in a pleasant environment that is adequate for the purpose of the visit (compatibility). Our results support the importance of large patches dedicated to a single function with a design that offers the possibility for seclusion and privacy, or the need to allocate specific functions to smaller parks connected in a multifunctional network of greenspaces in contrast to networks of one-size-fits-all multifunctional greenspaces. Lee et al. (2008) found a similar relationship with patch density regarding the existence of street trees and neighborhood satisfaction, whereby residents were more satisfied with large and well-connected tree patches than highly fragmented and isolated patches. Lu et al. (2018) showed that street greenery promoted walkability and visits to greenspaces, and Astell-Burt and Feng (2019) found that a higher tree canopy was associated with better mental health. These findings demonstrate the importance of greenways and green corridors as connectors of greenspaces to create multifunctional networks and to contribute to enhancing restorativeness.

In terms of the biodiversity in parks, the proportion of evergreen species was identified as a driver of PR. Evergreen species are associated with a perception of higher visual and aesthetic quality (Eroglu et al. 2012, Du et al. 2016) and are preferred to deciduous species (e.g., Camacho-Cervantes et al. 2014, Kuper 2015), and landscape preferences are associated with a higher likelihood of mental restoration (Pazhouhanfar and Kamal 2014, Lindal and Hartig 2015). The color green is known to elicit positive emotional responses in terms of relaxation and calmness because of its association with nature (Hemphill 1996, Kaya and Epps 2004), and virtual scenes containing evergreen species were shown to have a higher restorative potential than defoliated scenes (Paddle and Gilliland 2016, Kuper 2020).

The perception of high levels of biodiversity and the emotional connection to the place had stronger effects on PR than land cover and the taxonomical or functional diversity indexes. BDP was indirectly determined by the diversity of tree leaf types, and this effect was completely mediated by the perception of tree species diversity. Trees are usually the most prominent feature of a park, so it is easy to conceive that people might associate biodiversity with tree species diversity and, thus, with the diversity of leaf forms, specifically the percentage of broadleaf species. Broadleaf species have a large variety of readily distinguishable leaf shapes and tree habits, whereas those with needle and scale leaves, mostly highly similar-looking conifers, are difficult to distinguish without a deeper analysis of their morphology (Welch 1991). Different leaf shapes and tree habits present various visual stimuli that attract attention and provide cues regarding the meaning of an object, which are essential in the perceptual process (Lindsay and Norman 1977). Preference for broadleaf species has been reported (Lee 2001), especially in regions where they predominate (Schraml and Volz 2009) such as the Mediterranean region, where the native vegetation is dominated by broadleaf evergreen shrubs and trees, but also in other regions, including Australia (Williams 2002). Broadleaf species mostly have oval and spreading canopies, which are also preferred to the conical or pyramidal shapes that are common for conifers (Summit and Sommer 1999, Lohr and Pearson-Mims 2006).

We also found partial mediation of the effects of species richness, NDVI, and tree evenness on BDP, and these mediating processes meant that people perceived differences in biodiversity essentially through visual cues. This finding supports the statement by de Vries and Snep (2019:167) that “perceived biodiversity is likely to depend strongly on the visibility of the different species, and on the extent to which they are perceived as being different.” Mediation analysis was used in several previous studies of the mechanisms that allow different environmental qualities (existing or perceived) to influence mental well-being outcomes (e.g., Nordh et al. 2009, Carrus et al. 2015, Lindal and Hartig 2015, Hipp et al. 2016, Marselle et al. 2016, Knez et al. 2018, Tabrizian et al. 2018, Wang et al. 2019). However, to the best of our knowledge, none of these previous studies conducted analyses to determine the mechanisms that influenced the actual perception of biodiversity or restorativeness.

Emotions play important roles in how humans perceive and categorize the surrounding environment (Brosch et al. 2010, 2013), and emotionally significant stimuli are more easily recognized and identified than neutral stimuli (Zeelenberg et al. 2006, Zadra and Clore 2011). It is important to understand how emotions and perceptions mediate the interactions between tangible biophysical features and mental restoration. A regular and continuous interaction with a space is important for creating meaning and a sense of belonging (place identity), which translates into an emotional connection to the place, i.e., so-called place attachment (Tuan 1975, Williams and Vaske 2003, Smaldone 2007). In our study, the emotional connection with a place had a positive effect on PR whereby regular users who visited spaces at least once a week and those who visited for longer periods scored higher for PR. These findings indicate that psychological restoration takes time and does not “just happen” as soon as one enters a greenspace. Previous studies obtained similar results and showed that quick visits (< 15 min) to greenspaces did not stimulate physiological restoration (Tyrväinen et al. 2014). A positive effect of personal association with a place on restorative perceptions has been described previously (Korpela and Ylén 2009, Menatti et al. 2019), and place identity as a dimension of place attachment also has mediating roles between naturalness and well-being (Knez et al. 2018) and between memory and mental restoration (Ratcliffe and Korpela 2016). In our study, emotional connection with a place did not have any significant mediating role between biodiversity or its perception and restorativeness.

Thus, promoting restorativeness in a urban greenspace requires the provision of clean greenspaces with evergreen and/or broadleaf tree species and well-preserved amenities and facilities that are compatible with the needs and desires of visitors, which should be inviting and welcoming for people who visit a place. Considering tree functional traits and not only tree species is important for allowing park managers to decide which species to use locally by combining visible traits with local preferences and ecological contexts. Participatory governance processes involving local communities and other stakeholders are important for understanding their preferences and needs, but also for truly involving them in the cooperative design of nature-based approaches to mental health restorativeness. This approach may be particularly important in less developed countries with more recent urbanization processes and less acculturated urban populations, which may tend to import solutions that are not adapted to the local situation. The importance of this approach for these countries is indicated by the small amount of evidence accumulated from low and middle-income countries (Shuvo et al. 2020) compared to the growing volume of research into greenspace-health relationships in high-income countries in Europe, North America, and Australia (Markevych et al. 2017). Citizen science may provide a useful tool for understanding local preferences (Krasny et al. 2014), particularly in communities with fewer resources, where conducting social surveys to advance scientific knowledge might not be feasible.

Our findings highlight the need for the close integration of ecological and social values in urban greenspace planning and management. A next step may involve assessing how different origins and cultural constructs influence preferences, meanings, and values, and thus, the perception of biodiversity and restorativeness. This type of approach may help to address questions regarding environmental justice and equity and to bring these issues into governance processes.

The biocultural diversity (BCD) concept provides a valuable and much needed social-ecological lens through which to study the dynamics of urban greenspaces, whereby it addresses the interrelatedness between people and nature, and it is expressed by the diverse ways people live, experience, perceive, and value their natural environment. BCD can be recognized easily in cities (Elands et al. 2019) where human activity has shaped urban nature over time, and the materialized BCD and lived BCD represent two dimensions of that interaction. Materialized BCD is related to tangible manifestations represented by quantifiable discrete objects as ecological communities or cultural artifacts, and lived BCD is concerned with the ways that people relate to the biophysical environment through their experiences, emotions, and perceptions. Our results fully agree with this concept because they show that restorativeness and BDP are closely interlinked, with both dependent on the characteristics of place (materialized BCD) and on its perception and evaluation (lived BCD). By recognizing the mutual influences and interconnections between natural and cultural realms and the need to integrate social dynamics in urban ecosystems management, we consider that the BCD concept is useful for assisting the planning, designing, and management of greenspaces that are ecologically sound and that also provide locally adapted solutions to address a population’s well-being. To help implement the BCD framework in urban contexts, we recently proposed an innovative indicator-based tool to assist decision making (Gonçalves et al. 2021).


According to our results, the biophysical characteristics and user perceptions of parks were important for PR. The presence of large land-cover patches, evergreen tree species, and an adequate number of well-maintained amenities were considered the most restorative features. However, the personal experience of a space and its qualities, as well as the emotional connection with a space and, importantly, the perception of its biodiversity were crucial for this process. People perceived the existing biodiversity based on visual cues such as the diversity of leaf forms in broadleaf species, and this process was almost completely mediated by the perception of tree diversity. These findings can be directly translated into planning and management practices by acknowledging the importance of biodiversity-related attributes of greenspaces, i.e., tree functional traits, as well as promoting restorativeness for users of urban parks and recognizing the importance of using standard ecological methods for assessing biodiversity in urban greenspaces and its influence on human well-being.


Responses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a response, follow this link. To read responses already accepted, follow this link.


We thank all participants and research assistants who helped during the experiment. This research was supported by the European Commission, 7th Framework Programme Grant GREEN SURGE collaborative project FP7-ENV.2013.6.2-5-603567, and by Fundação para a Ciência e Tecnologia (FCT) through a doctoral scholarship to P. Gonçalves (PD/BD/106042/2015), and the Centre for Ecology, Evolution and Environmental Changes - cE3c (UID/BIA/00329/2013, 2015-2018 and UID/BIA/00329/2019). Funding sources were not involved in any decisions made during this study.


The data that support the findings of this study are available on request from the corresponding author, PG. The data are not publicly available because it they are still under analysis.


Andersson, E., and S. Barthel. 2016. Memory carriers and stewardship of metropolitan landscapes. Ecological Indicators 70:606-614.

Astell-Burt, T., and X. Feng. 2019. Association of urban greenspace with mental health and general health among adults in Australia. JAMA Network Open 2(7):e198209.

Baron, R. M., and D. A. Kenny. 1986. The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51(6):1173-1182.

Botzat, A., L. K. Fischer, and I. Kowarik. 2016. Unexploited opportunities in understanding liveable and biodiverse cities. A review on urban biodiversity perception and valuation. Global Environmental Change 39:220-233.

Brosch, T., G. Pourtois, and D. Sander. 2010. The perception and categorisation of emotional stimuli: a review. Cognition and Emotion 24(3):377-400.

Brosch, T., K. R. Scherer, D. Grandjean, and D. Sander. 2013. The impact of emotion on perception, attention, memory, and decision-making. Swiss Medical Weekly 143:w13786.

Camacho-Cervantes, M., J. E. Schondube, A. Castillo, and I. MacGregor-Fors. 2014. How do people perceive urban trees? Assessing likes and dislikes in relation to the trees of a city. Urban Ecosystems 17:761-773.

Carrus, G., R. Lafortezza, G. Colangelo, I. Dentamaro, M. Scopelliti, and G. Sanesi. 2013. Relations between naturalness and perceived restorativeness of different urban greenspaces. PsyEcology 4(3):227-244.

Carrus, G., M. Scopelliti, R. Lafortezza, G. Colangelo, F. Ferrini, F. Salbitano, M. Agrimi, L. Portoghesi, P. Semenzato, and G. Sanesi. 2015. Go greener, feel better? The positive effects of biodiversity on the well-being of individuals visiting urban and peri-urban green areas. Landscape and Urban Planning 134:221-228.

Cattell, V., N. Dines, W. Gesler, and S. Curtis. 2008. Mingling, observing, and lingering: everyday public spaces and their implications for well-being and social relations. Health and Place 14(3):544-561.

Dallimer, M., K. N. Irvine, A. M. J. Skinner, Z. G. Davies, J. R. Rouquette, L. L. Maltby, P. H. Warren, P. R. Armsworth and K. J. Gaston. 2012. Biodiversity and the feel-good factor: understanding associations between self-reported human well-being and species richness. Bioscience 62(1):47-55.

de Vries, S., and R. Snep. 2019. Biodiversity in the context of ‘biodiversity – mental health’ research. Pages 159-173 in M. R. Marselle, J. Stadler, H. Korn, K. N. Irvine, and A. Bonn, editors. Biodiversity and health in the face of climate change. Springer, Cham, Switzerland.

Deng, L., X. Li, H. Luo, E.-K. Fu, J. Ma, L.-X. Sun, Z. Huang, S.-Z. Cai, and Y. Jia. 2020. Empirical study of landscape types, landscape elements and landscape components of the urban park promoting physiological and psychological restoration. Urban Forestry and Urban Greening 48:126488.

Dinnie, E., K. M. Brown, and S. Morris. 2013. Community, cooperation and conflict: negotiating the social well-being benefits of urban greenspace experiences. Landscape and Urban Planning 112:1-9.

Du, H., H. Jiang, X. Song, D. Zhan, and Z. Bao. 2016. Assessing the visual aesthetic quality of vegetation landscape in urban greenspace from a visitor’s perspective. Journal of Urban Planning and Development 142(3):04016007.

Elands, B. H. M., K. Vierikko, E. Andersson, L. K. Fischer, P. Gonçalves, D. Haase, I. Kowarik, A. C. Luz, J. Niemelä, M. Santos-Reis, and K. F. Wiersum. 2019. Biocultural diversity: a novel concept to assess human-nature interrelations, nature conservation and stewardship in cities. Urban Forestry and Urban Greening 40:29-34.

Elmqvist, T., M. Fragkias, J. Goodness, B. Güneralp, P. J. Marcotullio, R. I. McDonald, S. Parnell, M. Schewenius, M. Sendstad, K. C. Seto, and C. Wilkinson, editors. 2013. Urbanization, biodiversity and ecosystem services: challenges and opportunities: a global assessment. Springer, Dordrecht, The Netherlands.

Elzinga, C. L., D. W. Salzer, and J. W. Willoughby. 2001. Measuring and monitoring plant populations. BLM Technical Reference 1730-1. Bureau of Land Management, Denver, Colorado, USA.

Enquist, B. J., R. Condit, R. K. Peet, M. Schildhauer, and B. M. Thiers. 2016. Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity. PeerJ Preprints 4:e2615v2

Eroğlu, E., H. Müderrisoğlu, and G. A. Kesim. 2012. The effect of seasonal change of plants compositions on visual perception. Journal of Environmental Engineering and Landscape Management 20(3):196-205.

Freund, R. J., W. J. Wilson, and D. L. Mohr. 2010. Statistical methods. Third edition. Academic Press, Burlington, Massachusetts, USA.

Frumkin, H., G. N. Bratman, S. J. Breslow, B. Cochran, P. H. Kahn Jr, J. J. Lawler, P. S. Levin, P. S. Tandon, U. Varanasi, K. L. Wolf, and S. A. Wood. 2017. Nature contact and human health: a research agenda. Environmental Health Perspectives 125(7):075001.

Fuller, R. A., K. N. Irvine, P. Devine-Wright, P. H. Warren, and K. J. Gaston. 2007. Psychological benefits of greenspace increase with biodiversity. Biology Letters 3(4):390-394.

Galea, S., N. Freudenberg, and D. Vlahov. 2005. Cities and population health. Social Science and Medicine 60(5):1017-1033.

Gonçalves, P., K. Vierikko, B. Elands, D. Haase, A. C. Luz, and M. Santos-Reis. 2021. Biocultural diversity in an urban context: an indicator-based decision support tool to guide the planning and management of green infrastructure. Environmental and Sustainability Indicators 11:100131.

Hajrasoulih, A., V. del Rio, J. Francis, and J. Edmondson. 2018. Urban form and mental wellbeing: scoping a theoretical framework for action. Journal of Urban Design and Mental Health 5:10. [online] URL:

Hall, C. R., and M. J. Knuth. 2019a. An update of the literature supporting the well-being benefits of plants: a review of the emotional and mental health benefits of plants. Journal of Environmental Horticulture 37(1):30-38.

Hall, C. R., and M. J. Knuth. 2019b. An update of the literature supporting the well-being benefits of plants: part 2 physiological health benefits. Journal of Environmental Horticulture 37(2):63-73.

Hartig, T. 2004. Restorative environments. Pages 273-279 in C. Spielberger, editor. Encyclopedia of applied psychology. Academic Press, San Diego, California, USA.

Hartig, T., K. Korpela, G. W. Evans, and T. Gärling. 1997. A measure of restorative quality in environments. Scandinavian Housing and Planning Research 14(4):175-194.

Hayes, A. F., and N. J. Rockwood. 2017. Regression-based statistical mediation and moderation analysis in clinical research: observations, recommendations, and implementation. Behaviour Research and Therapy 98:39-57.

Hemphill, M. 1996. A note on adults’ color–emotion associations. Journal of Genetic Psychology 157(3):275-280.

Hidalgo, M. C., R. Berto, M. Paz Galindo, and A. Getrevi. 2006. Identifying attractive and unattractive urban places: categories, restorativeness and aesthetic attributes. Medio Ambiente Y Comportamiento Humano 7(2):115-133.

Hipp, J. A., G. B. Gulwadi, S. Alves, and S. Sequeira. 2016. The relationship between perceived greenness and perceived restorativeness of university campuses and student-reported quality of life. Environment and Behavior 48(10):1292-1308.

Hosmer, D. W., and S. Lemeshow. 2000. Applied logistic regression. Second edition. Wiley, Hoboken, New Jersey, USA.

Houlden, V., S. Weich, J. P. de Albuquerque, S. Jarvis, and K. Rees. 2018. The relationship between greenspace and the mental wellbeing of adults: a systematic review. Plos One 13(9):e0203000.

Johansson, M., M. Gyllin, J. Witzell, and M. Küller. 2014. Does biological quality matter? Direct and reflected appraisal of biodiversity in temperate deciduous broad-leaf forest. Urban Forestry and Urban Greening 13(1):28-37.

Kaplan, R., and S. Kaplan. 1989. The experience of nature: a psychological perspective. Cambridge University Press, Cambridge, UK.

Kattge, J., S. Díaz, S. Lavorel, I. C. Prentice, P. Leadley, G. Bӧnisch, E. Garnier, M. Westoby, P. B. Reich, I. J. Wright, J. H. C. Cornelissen, C. Violle, S. P. Harrison, P. M. van Bodegom, M. Reichstein, B. J. Enquist, N. A. Soudzilovskaia, D. D. Ackerly, M. Anand, O. Atkin, M. Bahn, T. R. Baker, D. Baldocchi, R. Bekker, C. C. Blanco, B. Blonder, W. J. Bond, R. Bradstock, D. E. Bunker, F. Casanoves, J. Cavender-Bares, J. Q. Chambers, F. S. Chapin III, J. Chave, D. Coomes, W. K. Cornwell, J. M. Craine, B. H. Dobrin, L. Duarte, W. Durka, J. Elser, G. Esser, M. Estiarte, W. F. Fagan, J. Fang, F. Fernández-Méndez, A. Fidelis, B. Finegan, O. Flores, H. Ford, D. Frank, G. T. Freschet, N. M. Fyllas, R. V. Gallagher, W. A. Green, A. G. Gutierrez, T. Hickler, S. I. Higgins, J. G. Hodgson, A. Jalili, S. Jansen, C. A. Joly, A. J. Kerkhoff, D. Kirkup, K. Kitajima, M. Kleyer, S. Klotz, J. M. H. Knops, K. Kramer, I. Kühn, H. Kurokawa, D. Laughlin, T. D. Lee, M. Leishman, F. Lens, T. Lenz, S. L. Lewis, J. Lloyd, J. Llusià, F. Louault, S. Ma, M. D. Mahecha, P. Manning, T. Massad, B. E. Medlyn, J. Messier, A. T. Moles, S. C. Müller, K. Nadrowski, S. Naeem, Ü Niinemets, S. Nöllert, A. Nüske, R. Ogaya, J. Oleksyn, V. G. Onipchenko, Y. Onoda, J. Ordoñez, G. Overbeck, W. A. Ozinga, S. Patiño, S. Paula, J. G. Pausas, J. Peñuelas, O. L. Phillips, V. Pillar, H. Poorter, L. Poorter, P. Poschlod, A. Prinzing, R. Proulx, A. Rammig, S. Reinsch, B. Reu, L. Sack, B. Salgado-Negret, J. Sardans, S. Shiodera, B. Shipley, A. Siefert, E. Sosinski, J.-F. Soussana, E. Swaine, N. Swenson, K. Thompson, P. Thornton, M. Waldram, E. Weiher, M. White, S. White, S. J. Wright, B. Yguel, S. Zaehle, A. E. Zanne, and C. Wirth. 2011. TRY - a global database of plant traits. Global Change Biology 17(9):2905-2935. .

Kaya, N., and H. H. Epps. 2004. Relationship between color and emotion: a study of college students. College Student Journal 38(3):396-405.

Knez, I., Å. Ode Sang, B. Gunnarsson, and M. Hedblom. 2018. Wellbeing in urban greenery: the role of naturalness and place identity. Frontiers in Psychology 9:491.

Korpela, K. M., and M. P. Ylén. 2009. Effectiveness of favorite-place prescriptions: a field experiment. American Journal of Preventive Medicine 36(5):435-438.

Krasny, M. E., A. Russ, K. G. Tidball, T. Elmqvist. 2014. Civic ecology practices: participatory approaches to generating and measuring ecosystem services in cities. Ecosystem Services 7:177-186.

Kuper, R. 2015. Examining the visual effects of plant foliation and vegetative winter dormancy on preference and mystery. Landscape Journal 34(2):139-159.

Kuper, R. 2020. Preference and restorative potential for landscape models that depict diverse arrangements of defoliated, foliated, and evergreen plants. Urban Forestry and Urban Greening 48:126570.

Laliberté, E., and P. Legendre. 2010. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91(1):299-305.

Laliberté, E., P. Legendre, and B. Shipley. 2014. FD: measuring functional diversity (FD) from multiple traits, and other tools for functional ecology. R package version 1.0-12. [online] URL:

Lee, S.-W., C. D. Ellis, B.-S. Kweon, and S.-K. Hong. 2008. Relationship between landscape structure and neighborhood satisfaction in urbanized areas. Landscape and Urban Planning 85(1):60-70.

Lee, T. R. 2001. Perceptions, attitudes and preferences in forests and woodlands. Forestry Commission Technical Paper 18. Forestry Commission, Edimburgh, UK. [online] URL:

Lindal, P. J., and T. Hartig. 2013. Architectural variation, building height, and the restorative quality of urban residential streetscapes. Journal of Environmental Psychology 33:26-36.

Lindal, P. J., and T. Hartig. 2015. Effects of urban street vegetation on judgments of restoration likelihood. Urban Forestry and Urban Greening 14(2):200-209.

Lindemann-Matthies, P., and E. Bose. 2008. How many species are there? Public understanding and awareness of biodiversity in Switzerland. Human Ecology 36:731-742.

Lindsay, P. H., and D. A. Norman. 1977. Human information processing: an introduction to psychology. Second edition. Academic Press, New York, New York, USA.

Lohr, V. I., and C. H. Pearson-Mims. 2006. Responses to scenes with spreading, rounded, and conical tree forms. Environment and Behavior 38(5):667-688.

Lu, Y., C. Sarkar, and Y. Xiao. 2018. The effect of street-level greenery on walking behavior: evidence from Hong Kong. Social Science and Medicine 208:41-49.

Luck, G. W., P. Davidson, D. Boxall, and L. Smallbone. 2011. Relations between urban bird and plant communities and human well-being and connection to nature. Conservation Biology 25(4):816-826.

Madureira, H., F. Nunes, J. V. Oliveira, and T. Madureira. 2018. Preferences for urban greenspace characteristics: a comparative study in three Portuguese cities. Environments 5(2):23.

Markevych, I., J. Schoierer, T. Hartig, A. Chudnovsky, P. Hystad, A. M. Dzhambov, S. de Vries, M. Triguero-Mas, M. Brauer, M. J. Nieuwenhuijsen, G. Lupp, E. A. Richardson, T. Astell-Burt, D. Dimitrova, X. Feng, M. Sadeh, M. Standl, J. Heinrich, and E. Fuertes. 2017. Exploring pathways linking greenspace to health: theoretical and methodological guidance. Environmental Research 158:301-317.

Marselle, M. R., K. N. Irvine, A. Lorenzo-Arribas, and S. L. Warber. 2015. Moving beyond green: exploring the relationship of environment type and indicators of perceived environmental quality on emotional well-being following group walks. International Journal of Environmental Research and Public Health 12(1):106-130.

Marselle, M. R., K. N. Irvine, A. Lorenzo-Arribas, and S. L. Warber. 2016. Does perceived restorativeness mediate the effects of perceived biodiversity and perceived naturalness on emotional well-being following group walks in nature? Journal of Environmental Psychology 46:217-232.

Marselle, M. R., D. Martens, M. Dallimer, and K. N. Irvine. 2019. Review of the mental health and well-being benefits of biodiversity. Pages 175-211 in M. R. Marselle, J. Stadler, H. Korn, K. N. Irvine, and A. Bonn, editors. Biodiversity and health in the face of climate change. Springer, Cham, Switzerland.

McGarigal, K., S. A. Cushman, and E. Ene. 2012. FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. [software] University of Massachusetts, Amherst, Massachusetts, USA. [online] URL:

Menardo, E., M. Brondino, R. Hall, and M. Pasini. 2021. Restorativeness in natural and urban environments: a meta-analysis. Psychological Reports 124(2):417-437.

Menatti, L., M. Subiza-Pérez, A. Villalpando-Flores, L. Vozmediano, and C. San Juan. 2019. Place attachment and identification as predictors of expected landscape restorativeness. Journal of Environmental Psychology 63:36-43.

Nordh, H., T. Hartig, C. M. Hagerhall, and G. Fry. 2009. Components of small urban parks that predict the possibility for restoration. Urban Forestry and Urban Greening 8(4):225-235.

Nordh, H., and K. Østby. 2013. Pocket parks for people – a study of park design and use. Urban Forestry and Urban Greening 12(1):12-17.

Norman, G. 2010. Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education 15:625-632.

Paddle, E., and J. Gilliland. 2016. Orange is the new green: exploring the restorative capacity of seasonal foliage in schoolyard trees. International Journal of Environmental Research and Public Health 13(5):497.

Pasini, M., R. Berto, M. Scopelliti, and G. Carrus. 2009. Measuring the restorative value of the environment: contribution to the validation of the Italian version of the Perceived Restorativeness Scale. Bollettino Di Psicologia Applicata 257:3-11.

Pazhouhanfar, M., and M. Kamal. 2014. Effect of predictors of visual preference as characteristics of urban natural landscapes in increasing perceived restorative potential. Urban Forestry and Urban Greening 13(1):145-151.

Peschardt, K. K., U. K. Stigsdotter, and J. Schipperrijn. 2016. Identifying features of pocket parks that may be related to health promoting use. Landscape Research 41(1):79-94.

QGIS Development Team. 2019. QGIS: a free and open source geographic information system. Open Source Geospatial Foundation, Chicago, Illinois, USA. [online] URL:

Qiong, O. U. 2017. A brief introduction to perception. Studies in Literature and Language 15(4):18-28.

Rantakokko, M., K. E. Keskinen, K. Kokko, and E. Portegijs. 2018. Nature diversity and well-being in old age. Aging Clinical and Experimental Research 30:527-532.

Ratcliffe, E., and K. M. Korpela. 2016. Memory and place attachment as predictors of imagined restorative perceptions of favourite places. Journal of Environmental Psychology 48:120-130.

Revelle, W. 2018. psych: procedures for psychological, psychometric, and personality research. R package version 1.8.12. [online] URL:

Schebella, M. F., D. Weber, L. Schultz, and P. Weinstein. 2019. The wellbeing benefits associated with perceived and measured biodiversity in Australian urban greenspaces. Sustainability 11(3):802.

Schraml, U., and K.-R. Volz. 2009. Do species matter? Valuable broadleaves as an object of public perception and policy. Pages 213-236 in H. Spiecker, S. Hein, K. Makkonen-Spiecker, and M. Thies, editors. Valuable broadleaved forests in Europe. Brill, Leiden, The Netherlands.

Scopelliti, M., G. Carrus, F. Cini, S. Mastandrea, F. Ferrini, R. Lafortezza, M. Agrimi, F. Salbitano, G. Sanesi, and P. Semenzato. 2012. Biodiversity, perceived restorativeness and benefits of nature: A study on the psychological processes and outcomes of on-site experiences in urban and peri-urban green areas in Italy. Pages 255-269 in S. Kabisch, A. Kunath, P. Schweizer-Ries, and A. Steinführer, editors. Vulnerability, risks, and complexity: impacts of global change on human habitats. Hogrefe, Gottingen, Germany.

Shuvo, F. K., X. Feng, S. Akaraci, and T. Astell-Burt. 2020. Urban greenspace and health in low and middle-income countries: a critical review. Urban Forestry and Urban Greening 52:126662.

Smaldone, D. 2007. The role of time in place attachment. Pages 47-56 in R. Burns and K. Robinson, compilers. Proceedings of the 2006 Northeastern Recreation Research Symposium. GTR-NRS-P-14. U.S. Department of Agriculture, Forest Service, Northern Research Station, Newtown Square, Pennsylvania, USA. [online] URL:

Summit, J., and R. Sommer. 1999. Further studies of preferred tree shapes. Environment and Behavior 31(4):550-576.

Sutherland, W. J. 2006. Ecological census techniques: a handbook. Second edition. Cambridge University Press, Cambridge, UK.

Tabachnick, B. G., and L. S. Fidell. 1996. Using multivariate statistics. Third edition. HarperCollins, New York, New York, USA.

Tabrizian, P., P. K. Baran, W. R. Smith, and R. K. Meentemeyer. 2018. Exploring perceived restoration potential of urban green enclosure through immersive virtual environments. Journal of Environmental Psychology 55:99-109.

Tuan, Y.-F. 1975. Place: an experiential perspective. Geographical Review 65(2):151-165.

Tyrväinen, L., A. Ojala, K. Korpela, T. Lanki, Y. Tsunetsugu, and T. Kagawa. 2014. The influence of urban green environments on stress relief measures: a field experiment. Journal of Environmental Psychology 38:1-9.

Van Swaay, C. A. M., T. Brereton, P. Kirkland, and M. S. Warren. 2012. Manual for butterfly monitoring. Report VS2012.010. De Vlinderstichting/Dutch Butterfly Conservation, Butterfly Conservation UK, and Butterfly Conservation Europe, Wageningen, The Netherlands. [online] URL:

Vierikko, K., P. Gonçalves, D. Haase, B. Elands, C. Ioja, M. Jaatsi, M. Pieniniemi, J. Lindgren, F. Grilo, M. Santos-Reis, J. Niemelä, and V. Yli-Pelkonen. 2020. Biocultural diversity (BCD) in European cities – interactions between motivations, experiences and environment in public parks. Urban Forestry and Urban Greening 48:126501.

Wang, R., M. Helbich, Y. Yao, J. Zhang, P. Liu, Y. Yuan, and Y. Liu. 2019. Urban greenery and mental wellbeing in adults: cross-sectional mediation analyses on multiple pathways across different greenery measures. Environmental Research 176:108535.

Wapner, S., and J. Demick. 2002. The increasing contexts in the study of environment behavior relations Pages 3-14 in R. B. Bechtel and A. Churchman, editors. Handbook of environmental psychology. Wiley, New York, New York, USA.

Weber, A. M., and J. Trojan. 2018. The restorative value of the urban environment: a systematic review of the existing literature. Environmental Health Insights 12:1-13.

Welch, H. J. 1991. The conifer manual: volume 1. Springer, Dordrecht, The Netherlands.

Wheeler, B. W., R. Lovell, S. L. Higgins, M. P. White, I. Alcock, N. J. Osborne, K. Husk, C. E. Sabel, and M. H. Depledge. 2015. Beyond greenspace: an ecological study of population general health and indicators of natural environment type and quality. International Journal of Health Geographics 14:17.

Wilkie, S., and A. Stavridou. 2013. Influence of environmental preference and environment type congruence on judgments of restoration potential. Urban Forestry and Urban Greening 12(2):163-170.

Williams, D. R., and J. J. Vaske. 2003. The measurement of place attachment: validity and generalizability of a psychometric approach. Forest Science 49(6):830-840. [online] URL:

Williams, K. 2002. Exploring resident preferences for street trees in Melbourne, Australia. Journal of Arboriculture 28(4):161-170.

Wolf, L. J., S. zu Ermgassen, A. Balmford, M. White, and N. Weinstein. 2017. Is variety the spice of life? An experimental investigation into the effects of species richness on self-reported mental well-being. Plos One 12(1):e0170225.

Wood, E., A. Harsant, M. Dallimer, A. Cronin de Chavez, R. R. C. McEachan, and C. Hassall. 2018. Not all greenspace is created equal: biodiversity predicts psychological restorative benefits from urban greenspace. Frontiers in Psychology 9:2320.

Zadra, J. R., and G. L. Clore. 2011. Emotion and perception: the role of affective information. Wiley Interdisciplinary Reviews: Cognitive Science 2(6):676-685.

Zeelenberg, R., E.-J. Wagenmakers, and M. Rotteveel. 2006. The impact of emotion on perception: bias or enhanced processing? Psychological Science 17(4):287-291.

Correspondent author:
Paula Gonçalves
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